Sign language discourse is an essential mode of daily communication for the
deaf and hard-of-hearing people. However, research on Bangla Sign Language
(BdSL) faces notable limitations, primarily due to the lack of datasets.
Recognizing wordlevel signs in BdSL (WL-BdSL) presents a multitude of
challenges, including the need for well-annotated datasets, capturing the
dynamic nature of sign gestures from facial or hand landmarks, developing
suitable machine learning or deep learning-based models with substantial video
samples, and so on. In this paper, we address these challenges by creating a
comprehensive BdSL word-level dataset named BdSLW60 in an unconstrained and
natural setting, allowing positional and temporal variations and allowing sign
users to change hand dominance freely. The dataset encompasses 60 Bangla sign
words, with a significant scale of 9307 video trials provided by 18 signers
under the supervision of a sign language professional. The dataset was
rigorously annotated and cross-checked by 60 annotators. We also introduced a
unique approach of a relative quantization-based key frame encoding technique
for landmark based sign gesture recognition. We report the benchmarking of our
BdSLW60 dataset using the Support Vector Machine (SVM) with testing accuracy up
to 67.6% and an attention-based bi-LSTM with testing accuracy up to 75.1%. The
dataset is available at this https URL and
the code base is accessible from this https URL

本文介绍了一种新的方法，基于一个全面的 Bangla 手势词级数据集（BdSLW60），通过在不受约束的自然环境中捕捉手势的动态特性，允许位置和时间上的变化，并允许手势使用者自由更改手势的手的主导性，进而实现手势识别任务。

BdSLW60: 一个词汇级别的孟加拉手语数据集

BdSLW60: A Word-Level Bangla Sign Language Dataset

Recent advances in Deep Learning and Computer Vision have been successfully
leveraged to serve marginalized communities in various contexts. One such area
is Sign Language - a primary means of communication for the deaf community.
However, so far, the bulk of research efforts and investments have gone into
American Sign Language, and research activity into low-resource sign languages
- especially Bangla Sign Language - has lagged significantly. In this research
paper, we present a new word-level Bangla Sign Language dataset - BdSL40 -
consisting of 611 videos over 40 words, along with two different approaches:
one with a 3D Convolutional Neural Network model and another with a novel Graph
Neural Network approach for the classification of BdSL40 dataset. This is the
first study on word-level BdSL recognition, and the dataset was transcribed
from Indian Sign Language (ISL) using the Bangla Sign Language Dictionary
(1997). The proposed GNN model achieved an F1 score of 89%. The study
highlights the significant lexical and semantic similarity between BdSL, West
Bengal Sign Language, and ISL, and the lack of word-level datasets for BdSL in
the literature. We release the dataset and source code to stimulate further
research.

本研究提供了一个新的词级孟加拉手语数据集（BdSL40），包含 611 个单词的 40 个视频，以及两种不同的方法：一种是基于 3D 卷积神经网络模型，另一种是基于图神经网络的方法，用于对 BdSL40 数据集进行分类。该研究还揭示了 BdSL、西孟加拉手语和印度手语之间的显著词汇和语义相似性，以及文献中对 BdSL 缺乏词级数据集的情况。我们发布了数据集和源代码以促进进一步的研究。

连接节点：利用时空图神经网络进行准确的孟加拉手语识别

Connecting the Dots: Leveraging Spatio-Temporal Graph Neural Networks  for Accurate Bangla Sign Language Recognition

In the domain of Bangla Sign Language (BdSL) interpretation, prior approaches
often imposed a burden on users, requiring them to spell words without hidden
characters, which were subsequently corrected using Bangla grammar rules due to
the missing classes in BdSL36 dataset. However, this method posed a challenge
in accurately guessing the incorrect spelling of words. To address this
limitation, we propose a novel real-time finger spelling system based on the
YOLOv5 architecture. Our system employs specified rules and numerical classes
as triggers to efficiently generate hidden and compound characters, eliminating
the necessity for additional classes and significantly enhancing user
convenience. Notably, our approach achieves character spelling in an impressive
1.32 seconds with a remarkable accuracy rate of 98\%. Furthermore, our YOLOv5
model, trained on 9147 images, demonstrates an exceptional mean Average
Precision (mAP) of 96.4\%. These advancements represent a substantial
progression in augmenting BdSL interpretation, promising increased inclusivity
and accessibility for the linguistic minority. This innovative framework,
characterized by compatibility with existing YOLO versions, stands as a
transformative milestone in enhancing communication modalities and linguistic
equity within the Bangla Sign Language community.

提出了一种基于 YOLOv5 架构的新型实时手指拼写系统，通过使用指定规则和数字类作为触发器，高效生成隐藏和复合字符，大大提高了用户便利性，同时取得了令人瞩目的字符拼写准确率和模型平均精度，为增强 BdSL 解释提供了实质性的进展，提升了 Bangla 手语社区的沟通方式和语言平等性。